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Applied Computational Thinking with Python

Applied Computational Thinking with Python - Second Edition

By : Sofía De Jesús, Martinez
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Applied Computational Thinking with Python

Applied Computational Thinking with Python

By: Sofía De Jesús, Martinez

Overview of this book

Computational thinking helps you to develop logical processing and algorithmic thinking while solving real-world problems across a wide range of domains. It's an essential skill that you should possess to keep ahead of the curve in this modern era of information technology. Developers can apply their knowledge of computational thinking to solve problems in multiple areas, including economics, mathematics, and artificial intelligence. This book begins by helping you get to grips with decomposition, pattern recognition, pattern generalization and abstraction, and algorithm design, along with teaching you how to apply these elements practically while designing solutions for challenging problems. You’ll then learn about various techniques involved in problem analysis, logical reasoning, algorithm design, clusters and classification, data analysis, and modeling, and understand how computational thinking elements can be used together with these aspects to design solutions. Toward the end, you will discover how to identify pitfalls in the solution design process and how to choose the right functionalities to create the best possible algorithmic solutions. By the end of this algorithm book, you will have gained the confidence to successfully apply computational thinking techniques to software development.
Table of Contents (25 chapters)
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1
Part 1: An Introduction to Computational Thinking
10
Part 2: Applying Python and Computational Thinking
16
Part 3: Data Processing, Analysis, and Applications Using Computational Thinking and Python

Debugging

It is time for us to dedicate a little more time to debugging our algorithms. In Chapter 5, Errors, we looked at some of the errors we can get when writing code. In Chapter 8, Identifying Challenges with Solutions, we also saw some of the debugging tools that are built into Python to find errors in our code. Now, we are going to go deeper into algorithm debugging, looking at some problem situations and proposed algorithms, determining what causes the errors we are alerted to in the console, as well as those errors that we know exist because the outcome is not what is expected but we do not get an error message from Python. Some of these bugs may not be flagged by Python in any way, which is why we will now focus on some specific errors and how to avoid them. Debugging is a critical skill in programming, and it is impossible to cover all scenarios, but we will show some of the things you can look for when writing your solutions.

In this chapter, we will cover the following...

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Applied Computational Thinking with Python
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